THE IMPORTANCE OF INDIVIDUAL VARIATION IN DEFINING HABITAT SELECTION BY MOOSE IN NORTHERN BRITISH COLUMBIA
Bibliographic record
Abstract
ABSTRACT: Understanding resource use and selection has been central to many studies of ungu-late ecology. Global positioning satellite (GPS) collars, remote sensing, and geographic information systems (GIS) now make it easier to examine variation in use and selection by individuals. Resource selection functions, however, are commonly developed for global (all animals pooled) models and important information on individual variability may be lost. Using data from 14 female moose (Alces alces) collared in the Muskwa-Kechika Management Area of northern British Columbia, we examined differences among global and individual resource selection models for 5 seasons (winter, late winter, calving, summer, and fall). The global models indicated that moose selected for mid-elevations, and for deciduous burns and Carex sedge areas in all seasons. Resource selection models for individuals, however, indicated that no individuals selected the same attributes as the global models. We also ex-amined selection ratios among seasons with individual moose as replicates, and within individuals with bootstrapping techniques. We discuss the importance of considering individual variation in defining resource selection and habitat use by moose and contrast the results of selection ratios and resource selection models. We also use these data to illustrate some of the pitfalls that can be encountered using the 2 methodologies. ALCES VOL. 44: 7-20 (2008) Key words: Alces alces, habitat selection, home range, individual variation, resource selection, selec-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".